In a large number of documents, images are used as a backdrop to the actual document content in order to convey a message, mood or feeling. In these cases, other objects quite often are superimposed or overlaid over the image. For example in a life insurance brochure, the image of a “family” might serve as the backdrop to the actual life insurance advertisement. In order to overlay other objects or text over an image, the proper image regions have to be identified. More specifically, regions of the image that are of lower interest would be more likely regions to be overlaid. To date, such composition techniques have been manually accomplished by graphics designers and the like, making the creation of such layouts expensive. The method provided herein is directed to automatic detection of a Region of Interest (ROI), particularly a method that can aid in automatic creation of text or object overlay of images.
In order to overlay text or other objects onto an image, one has to avoid image areas that are of importance to an observer. For instance, it is highly undesirable to overlay the text over important image detail, such as the eyes of a person, the goods that are the subject of the advertisement, etc. The described method represents one way of automatically identifying regions for image overlay, by defining regions of interest, and thus, conversely, regions of little interest in an image—based on contrast metrics such as those developed in the context of automatic image enhancement (AIE).
In order to automatically overlay text over images for variable data applications, relatively large image areas have to be identified that have a low likelihood of being disturbed by the overlaid text, and vice versa. This also means that some small area of detail might be obstructed if it is in an otherwise “low-relevance” area. Simultaneously, good locations for text overlay will be ignored if they are too small. For this purpose, image areas of low relevance have to be found. There are clearly several criteria that influence the relevance of image areas and the following disclosure illustrates an example of one such criterion, namely the black and white contrast.
The following patents or publications are noted:
US 2001/0043741 by Mahoney et al, of Xerox Corporation, published Nov. 22, 2001, teaches the use of spatial separations between clusters of objects on an electronic work surface to automatically generate borders.
U.S. Pat. No. 6,711,291 to P. Stubler et al., issued Mar. 3, 2003, teaches a method for placing a human understandable item, such as a caption, text or a figurative element, in a digital image.
Empirical evidence suggests that regions of images that are important are relatively high in contrast. In other words, the image region makes use of essentially the entire dynamic range that is possible. The dynamic range of an image can be characterized by a histogram on the image, and preferred images tended to be characterized by histograms indicating that the entire dynamic range of the image is used. Hence, algorithms exist that modify an image in a way as to generate a histogram that covers the entire dynamic range. The most common algorithm is the histogram flattening/histogram equalization algorithm as described in R. C. Gonzales and B. A. Fittes, “Gray level transformation for interactive image enhancement,” Proc. Second Conference on Remotely Manned Systems 1975, E. L. Hall, “Almost uniform distributions for computer image enhancement,” IEEE Trans. Comput. C-23,207-208, 1974, W. K. Pratt, Digital Image Processing, Wiley, New York, 1978, and M. P. Ekstrom, Digital Image Processing Techniques, Academic Press, Orlando, 1984, J. C. Russ, The Image Processing Handbook, CRC Press, Boca Raton, 1992. Modifications to the histogram equalization techniques are known as adaptive histogram equalization (AHE) as in S. M. Pizer et al., “Adaptive histogram equalization and its variations,” Comput. Vision graphics and Image Proc. 39, 355-368, 1987 and the citations thereof. AHE again tends to work well when the aesthetic appearance of the image is not critical, but the information content of the image (that is, i.e. how well details are visible) is critical. When these goals and assumptions are not in place, histogram flattening and its known modifications work poorly.
In view of the need to automatically incorporate variable data into documents, as presented by today's printing technology, and the known relationship between image contrast and an observer's interest in an image, the method provided herein is believed to be advantageous. Disclosed in embodiments herein is a method for the automatic determination of a region of interest in an image, comprising the steps of: segmenting the image into a plurality of smaller regions, each region extending over a plurality of pixels; performing an analysis on each of said regions to characterize an aspect of the region relating to its level of importance in communicating information to a viewer; grouping adjacent regions having similar aspect characteristics; and identifying at least one group as a region of interest.
Also disclosed in embodiments herein is an automated document composition process, comprising the steps of: receiving a document including at least one image therein; specifying content to be overlaid on at least a portion of the image; identifying at least one low interest region of the image upon which the content may be overlaid; and automatically inserting at least a portion of the content into at least the one region.
The following disclosure will be characterized in connection with a preferred embodiment, however, it will be understood that there is no intent to limit the disclosure or scope to the embodiment described. On the contrary, the intent is to cover all alternatives, modifications, and equivalents as may be included within the spirit and scope of the appended claims.